There is a newer edition of this item:

Data analysis is of central importance in the education of scientists. This book offers a compact and readable introduction to techniques relevant to physical science students. The material is thoroughly integrated with the popular and powerful spreadsheet package Excel by Microsoft. Excel features of most relevance to the analysis of experimental data in the physical sciences are dealt with in some detail. Fully worked problems reinforce basic principles. Underlying assumptions and range of applicability of techniques are discussed, though detailed derivations of basic equations are mostly avoided or confined to the appendices.

Product Description

Review

"Overall I found the book excellent..." The Physicist

"Kirkup provides a very readable way for readers to learn basic principles of data analysis for the physical sciences and incoporates spreadsheets as flexible and powerful utilities...This excellent resource blends the power and utility of a popular spreadsheet package with relevant data analysis techniques and successfully combines content, relevance, and access to contemporary data analysis tools." Choice

Book Description

Data Analysis with Excel introduces techniques that are centrally important in the education of physical science students. Methods of data analysis are illustrated using the powerful spreadsheet package, Excel. Basic principles are reinforced using fully worked problems, and the underlying assumptions and range of applicability of techniques are discussed. Online support for the book covers further relevant topics. The book is suitable for undergraduate students in the physical sciences, but will also appeal to graduate students and researchers looking for an introduction to statistical techniques and the use of spreadsheets.

Most helpful customer reviews

If you are doing an engineering statistics course this book is of a hell of a lot more value than Engineering Statistics by Hubele, Montgomery and Runger.This book teaches you how to do statistics using excel.Should be aplicable for most statistics but is of greatassistance if your doing Engineering statistics and get stuck without much support.

Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com:
2 reviews

21 of 21 people found the following review helpful

A disappointing bookNov. 12 2004

By
A second reader
- Published on Amazon.com

Format: Paperback

This is an introductory book on Excel for physical scientists. Cambridge University Press deserves a compliment for a beautifully produced volume. Unfortunately, its contents are disappointing, because the text contains serious errors and omissions. The most obvious error is the statement, on page 308, that Excel does not provide built in facilities for fitting equations to data using nonlinear least squares. Excel does provide these, in the form of Solver, but the reader will look in vain for any mention of Solver in this book. (Figure 9.1 on page 365 shows that the author indeed has not bothered to activate the Solver Add-in.) The most serious omission is that the existence of user-definable functions and macros is not mentioned either. This leaves out two of the most powerful features of Excel: nonlinear least squares, and user programmability.

Another major problem with this book is that it doesn't show the reader how to use the spreadsheet effectively, but often goes out of its way to make easy things difficult. The almost exclusive emphasis in this book is on least squares methods, yet these are handled quite clumsily. On page 244, e.g., the linear correlation coefficient is computed from its formula by calculating the necessary sums, rather than by taking advantage of the fact that Linest, Regression, and Trendline all provide this parameter or its square. On page 284 the reader is shown the matrix algebra for fitting data to a parabola, and then told that "The built in matrix functions of Excel are well suited to estimating parameters in linear least squares problems", as if Linest, Regression, and Trendline are not there to take care of such tedious data manipulations. Likewise, on page 290, the user is not informed that Linest and Regression can also do multivariate analysis, but instead is instructed to do this the hard way, again by setting up and solving matrix equations. It is as if the author hasn't quite figured out yet that the spreadsheet has several built-in facilities specifically designed to make such least squares problems user-friendly.

In comparison with other books vying for the scientific spreadsheet market it is difficult to come up with any area in which Kirkup's book has the edge over its competitors: Billo (2nd ed., Wiley, 2001), Bloch (2nd ed., Wiley, 2003), de Levie (Oxford, 2004), Gottfried (2nd ed., McGraw-Hill, 2002), Liengme (3rd ed., Newnes, 2002), and Orvis (2nd ed., Sybex, 1996) all provide much more useful information, and don't make their readers jump through unnecessary hoops either.

1 of 4 people found the following review helpful

A must for engineering statisticsOct. 19 2003

By
A Customer
- Published on Amazon.com

Format: Paperback

If you are doing an engineering statistics course this book is of a hell of a lot more value than Engineering Statistics by Hubele, Montgomery and Runger.This book teaches you how to do statistics using excel.Should be aplicable for most statistics but is of greatassistance if your doing Engineering statistics and get stuck without much support.